Geotagged data can be used to describe regions in the world and discover
local themes. However, not all data produced within a region is necessarily
specifically descriptive of that area. To surface the content that is
characteristic for a region, we present the geographical hierarchy model (GHM),
a probabilistic model based on the assumption that data observed in a region is
a random mixture of content that pertains to different levels of a hierarchy.
We apply the GHM to a dataset of 8 million Flickr photos in order to
discriminate between content (i.e., tags) that specifically characterizes a
region (e.g., neighborhood) and content that characterizes surrounding areas or
more general themes. Knowledge of the discriminative and non-discriminative
terms used throughout the hierarchy enables us to quantify the uniqueness of a
given region and to compare similar but distant regions. Our evaluation
demonstrates that our model improves upon traditional Naive Bayes
classification by 47% and hierarchical TF-IDF by 27%. We further highlight the
differences and commonalities with human reasoning about what is locally
characteristic for a neighborhood, distilled from ten interviews and a survey
that covered themes such as time, events, and prior regional knowledgeComment: Accepted in WWW 2015, 2015, Florence, Ital
